In [1]:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")


import plotly.express as px
from plotly.subplots import make_subplots
import plotly.figure_factory as ff
In [3]:
insurance_claim=pd.read_csv("insurance_claims.csv")
insurance_claim.head()
Out[3]:
months_as_customer age policy_number policy_bind_date policy_state policy_csl policy_deductable policy_annual_premium umbrella_limit insured_zip ... police_report_available total_claim_amount injury_claim property_claim vehicle_claim auto_make auto_model auto_year fraud_reported _c39
0 328 48 521585 2014-10-17 OH 250/500 1000 1406.91 0 466132 ... YES 71610 6510 13020 52080 Saab 92x 2004 Y NaN
1 228 42 342868 2006-06-27 IN 250/500 2000 1197.22 5000000 468176 ... ? 5070 780 780 3510 Mercedes E400 2007 Y NaN
2 134 29 687698 2000-09-06 OH 100/300 2000 1413.14 5000000 430632 ... NO 34650 7700 3850 23100 Dodge RAM 2007 N NaN
3 256 41 227811 1990-05-25 IL 250/500 2000 1415.74 6000000 608117 ... NO 63400 6340 6340 50720 Chevrolet Tahoe 2014 Y NaN
4 228 44 367455 2014-06-06 IL 500/1000 1000 1583.91 6000000 610706 ... NO 6500 1300 650 4550 Accura RSX 2009 N NaN

5 rows × 40 columns

In [5]:
insurance_claim.describe()
Out[5]:
months_as_customer age policy_number policy_deductable policy_annual_premium umbrella_limit insured_zip capital-gains capital-loss incident_hour_of_the_day number_of_vehicles_involved bodily_injuries witnesses total_claim_amount injury_claim property_claim vehicle_claim auto_year _c39
count 1000.000000 1000.000000 1000.000000 1000.000000 1000.000000 1.000000e+03 1000.000000 1000.000000 1000.000000 1000.000000 1000.00000 1000.000000 1000.000000 1000.00000 1000.000000 1000.000000 1000.000000 1000.000000 0.0
mean 203.954000 38.948000 546238.648000 1136.000000 1256.406150 1.101000e+06 501214.488000 25126.100000 -26793.700000 11.644000 1.83900 0.992000 1.487000 52761.94000 7433.420000 7399.570000 37928.950000 2005.103000 NaN
std 115.113174 9.140287 257063.005276 611.864673 244.167395 2.297407e+06 71701.610941 27872.187708 28104.096686 6.951373 1.01888 0.820127 1.111335 26401.53319 4880.951853 4824.726179 18886.252893 6.015861 NaN
min 0.000000 19.000000 100804.000000 500.000000 433.330000 -1.000000e+06 430104.000000 0.000000 -111100.000000 0.000000 1.00000 0.000000 0.000000 100.00000 0.000000 0.000000 70.000000 1995.000000 NaN
25% 115.750000 32.000000 335980.250000 500.000000 1089.607500 0.000000e+00 448404.500000 0.000000 -51500.000000 6.000000 1.00000 0.000000 1.000000 41812.50000 4295.000000 4445.000000 30292.500000 2000.000000 NaN
50% 199.500000 38.000000 533135.000000 1000.000000 1257.200000 0.000000e+00 466445.500000 0.000000 -23250.000000 12.000000 1.00000 1.000000 1.000000 58055.00000 6775.000000 6750.000000 42100.000000 2005.000000 NaN
75% 276.250000 44.000000 759099.750000 2000.000000 1415.695000 0.000000e+00 603251.000000 51025.000000 0.000000 17.000000 3.00000 2.000000 2.000000 70592.50000 11305.000000 10885.000000 50822.500000 2010.000000 NaN
max 479.000000 64.000000 999435.000000 2000.000000 2047.590000 1.000000e+07 620962.000000 100500.000000 0.000000 23.000000 4.00000 2.000000 3.000000 114920.00000 21450.000000 23670.000000 79560.000000 2015.000000 NaN
In [7]:
insurance_claim.dtypes
Out[7]:
months_as_customer               int64
age                              int64
policy_number                    int64
policy_bind_date                object
policy_state                    object
policy_csl                      object
policy_deductable                int64
policy_annual_premium          float64
umbrella_limit                   int64
insured_zip                      int64
insured_sex                     object
insured_education_level         object
insured_occupation              object
insured_hobbies                 object
insured_relationship            object
capital-gains                    int64
capital-loss                     int64
incident_date                   object
incident_type                   object
collision_type                  object
incident_severity               object
authorities_contacted           object
incident_state                  object
incident_city                   object
incident_location               object
incident_hour_of_the_day         int64
number_of_vehicles_involved      int64
property_damage                 object
bodily_injuries                  int64
witnesses                        int64
police_report_available         object
total_claim_amount               int64
injury_claim                     int64
property_claim                   int64
vehicle_claim                    int64
auto_make                       object
auto_model                      object
auto_year                        int64
fraud_reported                  object
_c39                           float64
dtype: object
In [21]:
insurance_claim=insurance_claim.replace("?",np.NaN)
In [23]:
round(100*(insurance_claim.isnull().sum()/len(insurance_claim.index)),2)
Out[23]:
months_as_customer              0.0
age                             0.0
policy_number                   0.0
policy_bind_date                0.0
policy_state                    0.0
policy_csl                      0.0
policy_deductable               0.0
policy_annual_premium           0.0
umbrella_limit                  0.0
insured_zip                     0.0
insured_sex                     0.0
insured_education_level         0.0
insured_occupation              0.0
insured_hobbies                 0.0
insured_relationship            0.0
capital-gains                   0.0
capital-loss                    0.0
incident_date                   0.0
incident_type                   0.0
collision_type                 17.8
incident_severity               0.0
authorities_contacted           9.1
incident_state                  0.0
incident_city                   0.0
incident_location               0.0
incident_hour_of_the_day        0.0
number_of_vehicles_involved     0.0
property_damage                36.0
bodily_injuries                 0.0
witnesses                       0.0
police_report_available        34.3
total_claim_amount              0.0
injury_claim                    0.0
property_claim                  0.0
vehicle_claim                   0.0
auto_make                       0.0
auto_model                      0.0
auto_year                       0.0
fraud_reported                  0.0
dtype: float64
In [27]:
### Checking unique values for collosion type

round(100*(insurance_claim['collision_type'].value_counts(normalize=True,dropna=False)),2)
Out[27]:
collision_type
Rear Collision     29.2
Side Collision     27.6
Front Collision    25.4
NaN                17.8
Name: proportion, dtype: float64
In [29]:
### We can impute collosion type with mode i.e most occuring collosion type
### imputing

insurance_claim['collision_type']=insurance_claim['collision_type'].fillna(insurance_claim['collision_type'].mode()[0])
In [31]:
### Checking unique values for collosion type

round(100*(insurance_claim['property_damage'].value_counts(normalize=True,dropna=False)),2)
Out[31]:
property_damage
NaN    36.0
NO     33.8
YES    30.2
Name: proportion, dtype: float64
In [33]:
### If there was property loss,then the report would have been filed.So,we can consider missing value as "NO"
###Imputing with "NO"

insurance_claim['property_damage']=insurance_claim['property_damage'].fillna("NO")
In [35]:
### Checking unique values for collosion type

round(100*(insurance_claim['police_report_available'].value_counts(normalize=True,dropna=False)),2)
Out[35]:
police_report_available
NaN    34.3
NO     34.3
YES    31.4
Name: proportion, dtype: float64
In [37]:
### If there was police reported,then the report would have been filed.So,we can consider missing value as "NO"
###Imputing with "NO"

insurance_claim['police_report_available']=insurance_claim['property_damage'].fillna("NO")
In [39]:
### Checking missing value again

round(100*(insurance_claim.isnull().sum()/len(insurance_claim.index)),2)
Out[39]:
months_as_customer             0.0
age                            0.0
policy_number                  0.0
policy_bind_date               0.0
policy_state                   0.0
policy_csl                     0.0
policy_deductable              0.0
policy_annual_premium          0.0
umbrella_limit                 0.0
insured_zip                    0.0
insured_sex                    0.0
insured_education_level        0.0
insured_occupation             0.0
insured_hobbies                0.0
insured_relationship           0.0
capital-gains                  0.0
capital-loss                   0.0
incident_date                  0.0
incident_type                  0.0
collision_type                 0.0
incident_severity              0.0
authorities_contacted          9.1
incident_state                 0.0
incident_city                  0.0
incident_location              0.0
incident_hour_of_the_day       0.0
number_of_vehicles_involved    0.0
property_damage                0.0
bodily_injuries                0.0
witnesses                      0.0
police_report_available        0.0
total_claim_amount             0.0
injury_claim                   0.0
property_claim                 0.0
vehicle_claim                  0.0
auto_make                      0.0
auto_model                     0.0
auto_year                      0.0
fraud_reported                 0.0
dtype: float64
In [41]:
### Dropping those column which are not much sighnificance in oir analysis

insurance_claim.drop(["incident_hour_of_the_day",'insured_zip','policy_bind_date','incident_location'],axis=1,inplace=True)
In [43]:
### Checking values for each column

insurance_claim.nunique()
Out[43]:
months_as_customer              391
age                              46
policy_number                  1000
policy_state                      3
policy_csl                        3
policy_deductable                 3
policy_annual_premium           991
umbrella_limit                   11
insured_sex                       2
insured_education_level           7
insured_occupation               14
insured_hobbies                  20
insured_relationship              6
capital-gains                   338
capital-loss                    354
incident_date                    60
incident_type                     4
collision_type                    3
incident_severity                 4
authorities_contacted             4
incident_state                    7
incident_city                     7
number_of_vehicles_involved       4
property_damage                   2
bodily_injuries                   3
witnesses                         4
police_report_available           2
total_claim_amount              763
injury_claim                    638
property_claim                  626
vehicle_claim                   726
auto_make                        14
auto_model                       39
auto_year                        21
fraud_reported                    2
dtype: int64
In [45]:
### Total claim must be equal to sum of "injury_claim","property_claim","vehicle_claim"

insurance_claim[insurance_claim['total_claim_amount'] != insurance_claim['injury_claim']+insurance_claim['property_claim']+insurance_claim['vehicle_claim']].shape[0]
Out[45]:
0
In [47]:
### Others sanity check

print(insurance_claim[insurance_claim['months_as_customer']<0].shape[0])
print(insurance_claim[insurance_claim['age']<0].shape[0])
print(insurance_claim[insurance_claim['policy_annual_premium']<0].shape[0])
print(insurance_claim[insurance_claim['total_claim_amount']<0].shape[0])
print(insurance_claim[insurance_claim['umbrella_limit']<0].shape[0])
print(insurance_claim[insurance_claim['capital-gains']<0].shape[0])
print(insurance_claim[insurance_claim['number_of_vehicles_involved']<0].shape[0])
print(insurance_claim[insurance_claim['injury_claim']<0].shape[0])
print(insurance_claim[insurance_claim['property_claim']<0].shape[0])
print(insurance_claim[insurance_claim['vehicle_claim']<0].shape[0])

0
0
0
0
1
0
0
0
0
0
In [49]:
### Fetching that row

insurance_claim[insurance_claim['umbrella_limit']<0]
Out[49]:
months_as_customer age policy_number policy_state policy_csl policy_deductable policy_annual_premium umbrella_limit insured_sex insured_education_level ... witnesses police_report_available total_claim_amount injury_claim property_claim vehicle_claim auto_make auto_model auto_year fraud_reported
290 284 42 526039 OH 100/300 500 1338.54 -1000000 MALE Associate ... 1 YES 94160 8560 17120 68480 Chevrolet Malibu 1996 N

1 rows × 35 columns

In [51]:
### dropping above row

insurance_claim.drop(insurance_claim.index[290],inplace=True)
In [53]:
### changing Incident date into datetime format
insurance_claim['incident_date']=pd.to_datetime(insurance_claim['incident_date'],format='%Y-%m-%d')

### changing auto_year date into datetime format
insurance_claim['auto_year']=pd.to_datetime(insurance_claim['auto_year'],format='%Y')
In [56]:
### Checking datatypes again
insurance_claim.dtypes
Out[56]:
months_as_customer                      int64
age                                     int64
policy_number                           int64
policy_state                           object
policy_csl                             object
policy_deductable                       int64
policy_annual_premium                 float64
umbrella_limit                          int64
insured_sex                            object
insured_education_level                object
insured_occupation                     object
insured_hobbies                        object
insured_relationship                   object
capital-gains                           int64
capital-loss                            int64
incident_date                  datetime64[ns]
incident_type                          object
collision_type                         object
incident_severity                      object
authorities_contacted                  object
incident_state                         object
incident_city                          object
number_of_vehicles_involved             int64
property_damage                        object
bodily_injuries                         int64
witnesses                               int64
police_report_available                object
total_claim_amount                      int64
injury_claim                            int64
property_claim                          int64
vehicle_claim                           int64
auto_make                              object
auto_model                             object
auto_year                      datetime64[ns]
fraud_reported                         object
dtype: object
In [58]:
#outlier
plt.figure(figsize=[20,20])


num_col= ['months_as_customer','age','policy_deductable','policy_annual_premium','umbrella_limit','capital-gains','capital-loss','number_of_vehicles_involved','bodily_injuries','witnesses','total_claim_amount','injury_claim','property_claim','vehicle_claim']

c=1

for i in num_col:
    plt.subplot(4, 4, c)
    plt.xlabel(i)
    sns.boxplot(insurance_claim[i] , palette="Set2")
    c = c + 1
    
plt.show()
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In [66]:
insurance_claim['count']=1
In [60]:
#EDA
#plt.figure(figsize=[5,4])
fig = px.pie(insurance_claim,names='fraud_reported')
fig.show()
In [70]:
### Datasplitting into fraud and not fraud category

insurance_claim_f=insurance_claim[insurance_claim['fraud_reported']=='Y']
insurance_claim_nf=insurance_claim[insurance_claim['fraud_reported']=='N']
In [74]:
plt.figure(figsize=[15,20])
num_col=insurance_claim.describe().columns.to_list()

c=1

for i in num_col:
    plt.subplot(4, 4, 4)
    sns.distplot(insurance_claim_f[i],hist=False,label='Fraud')
    sns.distplot(insurance_claim_nf[i],hist=False,label='Not-Fraud')
    c = c + 1
    plt.legend()    
plt.show()
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In [76]:
fig = px.box(insurance_claim, x="policy_state", y="total_claim_amount", color="fraud_reported")
fig.show()
In [78]:
fig=px.scatter(insurance_claim,x='policy_annual_premium',y='total_claim_amount',color='fraud_reported',marginal_x='histogram',marginal_y='histogram',trendline='ols')
fig.show()
In [80]:
fig=px.scatter(insurance_claim,x='policy_annual_premium',y='property_claim',color='fraud_reported')
fig.show()
In [82]:
fig=px.scatter(insurance_claim,x='policy_annual_premium',y='vehicle_claim',color='fraud_reported')
fig.show()
In [84]:
fig=px.scatter(insurance_claim,x='policy_annual_premium',y='injury_claim',color='fraud_reported')
fig.show()
In [86]:
fig=px.scatter(insurance_claim,x='age',y='total_claim_amount',color='fraud_reported',marginal_x='box',marginal_y='violin',trendline='ols')
fig.show()
In [88]:
groupdf_is=insurance_claim.groupby(by=['insured_sex','fraud_reported']).count()
groupdf_is.reset_index(inplace=True)
groupdf_is=groupdf_is[['insured_sex','fraud_reported','count']]
groupdf_is.head()
Out[88]:
insured_sex fraud_reported count
0 FEMALE N 411
1 FEMALE Y 126
2 MALE N 341
3 MALE Y 121
In [90]:
fig=px.bar(groupdf_is,x='insured_sex',y='count',color='fraud_reported',text='count',pattern_shape_sequence=['|','/'],pattern_shape='fraud_reported')
fig.show()
In [92]:
groupdf_uu=insurance_claim.groupby(by=['umbrella_limit','fraud_reported']).count()
groupdf_uu.reset_index(inplace=True)
groupdf_uu=groupdf_uu[['umbrella_limit','fraud_reported','count']]
groupdf_uu.head(3)
Out[92]:
umbrella_limit fraud_reported count
0 0 N 611
1 0 Y 187
2 2000000 N 1
In [94]:
fig=px.bar(groupdf_uu,x='umbrella_limit',y='count',color='fraud_reported',text='count')
fig.show()
In [96]:
groupdf_el=insurance_claim.groupby(by=['insured_education_level','fraud_reported']).count()
groupdf_el.reset_index(inplace=True)
groupdf_el=groupdf_el[['insured_education_level','fraud_reported','count']]
groupdf_el.head()
Out[96]:
insured_education_level fraud_reported count
0 Associate N 110
1 Associate Y 34
2 College N 90
3 College Y 32
4 High School N 124
In [98]:
fig=px.bar(groupdf_el,x='insured_education_level',y='count',color='fraud_reported',text='count',title="Insurance Claim for different education Level")
fig.show()
In [100]:
groupdf_ic=insurance_claim.groupby(by=['insured_occupation','fraud_reported']).count()
groupdf_ic.reset_index(inplace=True)
groupdf_ic=groupdf_ic[['insured_occupation','fraud_reported','count']]
groupdf_ic.head()
Out[100]:
insured_occupation fraud_reported count
0 adm-clerical N 54
1 adm-clerical Y 11
2 armed-forces N 52
3 armed-forces Y 17
4 craft-repair N 52
In [102]:
fig=px.bar(groupdf_ic,x='insured_occupation',y='count',color='fraud_reported',text='count',title='Number of Claims diffrent Occupation')
fig.show()
In [104]:
groupdf_h=insurance_claim.groupby(by=['insured_hobbies','fraud_reported']).count()
groupdf_h.reset_index(inplace=True)
groupdf_h=groupdf_h[['insured_hobbies','fraud_reported','count']]
groupdf_h.head()
Out[104]:
insured_hobbies fraud_reported count
0 base-jumping N 36
1 base-jumping Y 13
2 basketball N 28
3 basketball Y 6
4 board-games N 34
In [106]:
fig=px.bar(groupdf_h,x='insured_hobbies',y='count',color='fraud_reported',text='count',title="Hobby Wise claim")
fig.show()
In [108]:
groupdf_rl=insurance_claim.groupby(by=['insured_relationship','fraud_reported']).count()
groupdf_rl.reset_index(inplace=True)
groupdf_rl=groupdf_rl[['insured_relationship','fraud_reported','count']]
groupdf_rl.head()
Out[108]:
insured_relationship fraud_reported count
0 husband N 135
1 husband Y 35
2 not-in-family N 129
3 not-in-family Y 45
4 other-relative N 125
In [110]:
fig=px.bar(groupdf_rl,x='insured_relationship',y='count',color='fraud_reported',text='count')
fig.show()
In [112]:
groupdf_it=insurance_claim.groupby(by=['incident_type','fraud_reported']).count()
groupdf_it.reset_index(inplace=True)
groupdf_it=groupdf_it[['incident_type','fraud_reported','count']]
groupdf_it.head()
Out[112]:
incident_type fraud_reported count
0 Multi-vehicle Collision N 305
1 Multi-vehicle Collision Y 114
2 Parked Car N 76
3 Parked Car Y 8
4 Single Vehicle Collision N 285
In [114]:
fig=px.bar(groupdf_it,x='incident_type',y='count',color='fraud_reported',text='count',title="Number of claims Vs. Incident type")
fig.show()
In [116]:
groupdf_ct=insurance_claim.groupby(by=['collision_type','fraud_reported']).count()
groupdf_ct.reset_index(inplace=True)
groupdf_ct=groupdf_ct[['collision_type','fraud_reported','count']]
groupdf_ct.head()
Out[116]:
collision_type fraud_reported count
0 Front Collision N 184
1 Front Collision Y 70
2 Rear Collision N 363
3 Rear Collision Y 107
4 Side Collision N 205
In [118]:
fig=px.bar(groupdf_ct,x='collision_type',y='count',color='fraud_reported',text='count',title="Nos. of Claim for different type of collision")
fig.show()
In [120]:
groupdf_ins=insurance_claim.groupby(by=['incident_severity','fraud_reported']).count()
groupdf_ins.reset_index(inplace=True)
groupdf_ins=groupdf_ins[['incident_severity','fraud_reported','count']]
groupdf_ins.head()
Out[120]:
incident_severity fraud_reported count
0 Major Damage N 108
1 Major Damage Y 167
2 Minor Damage N 316
3 Minor Damage Y 38
4 Total Loss N 244
In [122]:
fig=px.bar(groupdf_ins,x='incident_severity',y='count',color='fraud_reported',text='count',title="Number of claim Vs. Incident severity")
fig.show()
In [124]:
groupdf_ac=insurance_claim.groupby(by=['authorities_contacted','fraud_reported']).count()
groupdf_ac.reset_index(inplace=True)
groupdf_ac=groupdf_ac[['authorities_contacted','fraud_reported','count']]
groupdf_ac.head()
Out[124]:
authorities_contacted fraud_reported count
0 Ambulance N 138
1 Ambulance Y 57
2 Fire N 163
3 Fire Y 60
4 Other N 135
In [126]:
fig=px.bar(groupdf_ac,x='authorities_contacted',y='count',color='fraud_reported',text='count')
fig.show()
In [128]:
groupdf_st=insurance_claim.groupby(by=['incident_state','fraud_reported']).count()
groupdf_st.reset_index(inplace=True)
groupdf_st=groupdf_st[['incident_state','fraud_reported','count']]
groupdf_st.head()
Out[128]:
incident_state fraud_reported count
0 NC N 75
1 NC Y 34
2 NY N 204
3 NY Y 58
4 OH N 13
In [130]:
fig=px.bar(groupdf_st,x='incident_state',y='count',color='fraud_reported',text='count',title="Claims for different state")
fig.show()
In [132]:
groupdf_pd=insurance_claim.groupby(by=['property_damage','fraud_reported']).count()
groupdf_pd.reset_index(inplace=True)
groupdf_pd=groupdf_pd[['property_damage','fraud_reported','count']]
groupdf_pd.head()
Out[132]:
property_damage fraud_reported count
0 NO N 529
1 NO Y 169
2 YES N 223
3 YES Y 78
In [134]:
fig=px.bar(groupdf_pd,x='property_damage',y='count',color='fraud_reported',text='count')
fig.show()
In [136]:
groupdf_w=insurance_claim.groupby(by=['witnesses','fraud_reported']).count()
groupdf_w.reset_index(inplace=True)
groupdf_w=groupdf_w[['witnesses','fraud_reported','count']]
groupdf_w.head()
Out[136]:
witnesses fraud_reported count
0 0 N 199
1 0 Y 50
2 1 N 194
3 1 Y 63
4 2 N 176
In [138]:
fig=px.bar(groupdf_w,x='witnesses',y='count',color='fraud_reported',text='count',title="Nos. of claims vs No. of witnesses")
fig.show()
In [140]:
groupdf_am=insurance_claim.groupby(by=['auto_make','fraud_reported']).count()
groupdf_am.reset_index(inplace=True)
groupdf_am=groupdf_am[['auto_make','fraud_reported','count']]
groupdf_am.head()
Out[140]:
auto_make fraud_reported count
0 Accura N 55
1 Accura Y 13
2 Audi N 48
3 Audi Y 21
4 BMW N 52
In [142]:
fig=px.bar(groupdf_am,x='auto_make',y='count',color='fraud_reported',text='count',title="Claims for different auto make")
fig.show()
In [144]:
groupdf_atm=insurance_claim.groupby(by=['auto_model','fraud_reported']).count()
groupdf_atm.reset_index(inplace=True)
groupdf_atm=groupdf_atm[['auto_model','fraud_reported','count']]
groupdf_atm.head()
Out[144]:
auto_model fraud_reported count
0 3 Series N 17
1 3 Series Y 1
2 92x N 20
3 92x Y 8
4 93 N 20
In [146]:
fig=px.bar(groupdf_atm,x='auto_model',y='count',color='fraud_reported',text='count',title="Claims for diiferent auto model")
fig.show()
In [148]:
groupdf_atmm=insurance_claim.groupby(by=['auto_make','auto_model','fraud_reported']).count()
groupdf_atmm.reset_index(inplace=True)
groupdf_atmm=groupdf_atmm[['auto_make','auto_model','fraud_reported','count']]
groupdf_atmm['fraud_reported']=groupdf_atmm['fraud_reported'].apply(lambda x: 'Fraud Reported :YES' if x == 'Y' else 'Fraud Reported :NO')
groupdf_atmm.head()
Out[148]:
auto_make auto_model fraud_reported count
0 Accura MDX Fraud Reported :NO 28
1 Accura MDX Fraud Reported :YES 8
2 Accura RSX Fraud Reported :NO 11
3 Accura RSX Fraud Reported :YES 1
4 Accura TL Fraud Reported :NO 16
In [150]:
fig=px.bar(groupdf_atmm,x='auto_make',y='count',color='auto_model',text='count',hover_name='fraud_reported',title="Claims by Auto make amd Auto model")
fig.show()